Neural Networks for Predicting the Deterioration of Concrete Structures

1996 ◽  
pp. 415-431
Author(s):  
N. R. Buenfeld ◽  
N. M. Hassanein
Keyword(s):  
Neural Networks ◽  
Concrete Structures

Auto-detection of acoustic emission signals from cracking of concrete structures using convolutional neural networks: Upscaling from specimen

2021 ◽  
Vol 186 ◽  
pp. 115863
Author(s):  
Gyeol Han ◽  
Yong-Min Kim ◽  
Hyunwoo Kim ◽  
Tae-Min Oh ◽  
Ki-Il Song ◽  
...  
Keyword(s):  
Neural Networks ◽  
Acoustic Emission ◽  
Convolutional Neural Networks ◽  
Concrete Structures

A Non-Destructive Testing Based on Electromagnetic Measurements and Neural Networks for the Inspection of Concrete Structures

2011 ◽  
Vol 301-303 ◽  
pp. 597-602 ◽  
Author(s):  
Naasson P. de Alcantara ◽  
Danilo C. Costa ◽  
Diego S. Guedes ◽  
Ricardo V. Sartori ◽  
Paulo S. S. Bastos
Keyword(s):  
Neural Networks ◽  
Concrete Structures ◽  
Concrete Surface ◽  
Non Destructive Testing ◽  
Destructive Testing ◽  
Electromagnetic Devices ◽  
Electromagnetic Measurements ◽  
Steel Bars ◽  
Non Destructive ◽  
The Magnetic Field

This paper presents a new non-destructive testing (NDT) for reinforced concrete structures, in order to identify the components of their reinforcement. A time varying electromagnetic field is generated close to the structure by electromagnetic devices specially designed for this purpose. The presence of ferromagnetic materials (the steel bars of the reinforcement) immersed in the concrete disturbs the magnetic field at the surface of the structure. These field alterations are detected by sensors coils placed on the concrete surface. Variations in position and cross section (the size) of steel bars immersed in concrete originate slightly different values for the induced voltages at the coils.. The values ​​for the induced voltages were obtained in laboratory tests, and multi-layer perceptron artificial neural networks with Levemberg-Marquardt training algorithm were used to identify the location and size of the bar. Preliminary results can be considered very good.

scholarly journals A Crack Identification Method for Concrete Structures Using Improved U-Net Convolutional Neural Networks

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Wenting Qiao ◽  
Hongwei Zhang ◽  
Fei Zhu ◽  
Qiande Wu
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Data Augmentation ◽  
Concrete Beams ◽  
Concrete Structures ◽  
Reinforced Concrete Beams ◽  
Bending Test ◽  
Crack Identification ◽  
Identification Method ◽  
Proposed Model

The traditional method for detecting cracks in concrete bridges has the disadvantages of low accuracy and weak robustness. Combined with the crack digital image data obtained from bending test of reinforced concrete beams, a crack identification method for concrete structures based on improved U-net convolutional neural networks is proposed to improve the accuracy of crack identification in this article. Firstly, a bending test of concrete beams is conducted to collect crack images. Secondly, datasets of crack images are obtained using the data augmentation technology. Selected cracks are marked. Thirdly, based on the U-net neural networks, an improved inception module and an Atrous Spatial Pyramid Pooling module are added in the improved U-net model. Finally, the widths of cracks are identified using the concrete crack binary images obtained from the improved U-net model. The average precision of the test set of the proposed model is 11.7% higher than that of the U-net neural network segmentation model. The average relative error of the crack width of the proposed model is 13.2%, which is 18.6% less than that measured by using the ACTIS system. The results indicate that the proposed method is accurate, robust, and suitable for crack identification in concrete structures.

Inverse Analysis of Aesthetic Evaluation of Planted Concrete Structures by Neural Networks

1998 ◽  
Vol 13 (4) ◽  
pp. 255-264 ◽  
Author(s):  
Yasuo Chikata ◽  
Noboru Yasuda ◽  
Manabu Matsushima ◽  
Tameo Kobori
Keyword(s):  
Neural Networks ◽  
Inverse Analysis ◽  
Concrete Structures ◽  
Aesthetic Evaluation

Neural networks applications in concrete structures

2003 ◽  
Vol 81 (6) ◽  
pp. 373-381 ◽  
Author(s):  
Muhammad N.S. Hadi
Keyword(s):  
Neural Networks ◽  
Concrete Structures

scholarly journals APPLICATION OF ARTIFICIAL NEURAL NETWORKS FOR ASSESSING THE REINFORCEMENT OF REINFORCED-CONCRETE FLOOR SLABS

2019 ◽  
Author(s):  
A. Liashkevich
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Reinforced Concrete ◽  
Concrete Slab ◽  
Concrete Structures ◽  
Building Design ◽  
Reinforced Concrete Structures ◽  
Reinforced Concrete Slab ◽  
Artificial Neural

In this article the problem of assessment of working documentation quality in terms of trustworthiness of the calculation of area of main reinforcement of reinforced-concrete structures is reviewed. In spite of development of automated designing systems, no application solutions for fully automated check of quality of working documentation for reinforced-concrete structures as regards sufficiency and necessity of reinforcement of them have been proposed until now. Moreover, this rather routine procedure can be fully automated to exclude the subjective nature of its results. Artificial neural networks (ANN) constitute the most promising mathematical model for this purpose. There are known examples demonstrating the possibility of applying the ANN for various types of calculations and analysis of experimental data for reinforced-concrete structures. In particular, the ANN allows predicting the actual deformation parameters of reinforced-concrete structures with significantly greater accuracy than any of the current national design standards. The article presents the results of calculations of reinforcement and sag for various input parameters using the example of reinforced-concrete slab structure. Using the simplest ANN with one hidden layer over the entire training sample, the predicted values with sufficient accuracy for practical use were obtained. It has been established that ANN makes it possible to predict effectively not only values of the required reinforcement for slab structures, but also their deformation. Within the framework of BIM-technologies used currently in building design, the use of ANN to assess the quality of ready-made design documentation in terms of reinforcement will reduce considerably the cost and time of relevant examinations with significantly higher trustworthiness of their results.В статье рассмотрена задача оценки качества рабочей документации в части достоверности расчета площади рабочей арматуры железобетонных конструкций. Несмотря на развитие систем автоматизированного проектирования, до настоящего времени не предложено прикладных решений для полностью автоматизированной проверки качества рабочей документации железобетонных конструкций на предмет достаточности и необходимости их армирования. При этом эта весьма относительно рутинная процедура может быть полностью автоматизирована для исключения субъективного характера ее результатов. Наиболее перспективной математической моделью для этой цели являются искусственные нейронные сети (ИНС). Известны примеры, демонстрирующие возможность прикладного применения ИНС для различного рода расчетов и анализа экспериментальных данных для железобетонных конструкций. В частности, ИНС позволяет с существенно большей точностью прогнозировать фактические параметры деформирования железобетонных конструкций, чем любые из действующих национальных норм проектирования. В статье на примере железобетонной плитной конструкции приведены результаты расчетов армирования и прогиба при различных значениях входных параметров. С помощью простейшей ИНС с одним скрытым слоем по всей обучающей выборке получены прогнозные значения с достаточной для практического их использования точностью. Установлено, что ИНС позволяет достаточно эффективно прогнозировать не только значения требуемого армирования для плитных конструкций, но и их деформации. В рамках используемых в настоящее время в строительном проектировании BIM-технологий применение ИНС для оценки качества готовой проектной документации в части армирования позволит значительно сократить стоимость и сроки соответствующих экспертиз при существенно более высокой достоверности их результатов.

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